用于表示学习的联想压缩网络
Associative Compression Networks for Representation Learning
摘要：
本文介绍了关联压缩网络（ACNs），一个新的框架变分自动编码与神经网络。该系统不同于现有的变分自编码器（VAE），因为用于对每个代码进行建模的先验分布以来自数据集的类似代码为条件。在压缩术语中，这等同于使用由潜在空间中的接近度确定的排序来顺序地传输数据集。由于先验只需要考虑潜在空间中的局部变化，而不是全局变化，因此大大降低了编码成本，从而产生丰富的信息代码。至关重要的是，当使用强大的自回归解码器时，代码仍然提供信息，我们认为这对于正常的VAE来说是根本困难的。在MNIST，CIFAR-10，ImageNet和CelebA上的实验结果表明，ACNs发现了诸如对象类，写作风格，姿势和面部表情等高级潜在特征，这些特征可用于对数据进行聚类和分类，以及生成多样化和令人信服的样本。我们的结论是，ACNs是表示学习的一个有前途的新方向：一个远离IID建模，并朝着学习数据集作为一个整体的结构化描述。
英文摘要：
This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the dataset using an ordering determined by proximity in latent space. Since the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes. Crucially, the codes remain informative when powerful, autoregressive decoders are used, which we argue is fundamentally difficult with normal VAEs. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover high-level latent features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples. We conclude that ACNs are a promising new direction for representation learning: one that steps away from IID modelling, and towards learning a structured description of the dataset as a whole.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
